Preprint Prevalence of long-term symptoms varies by using different post-COVID-19 definitions in positively & negatively tested adults:PRIME study, 2023, Pagen

Prevalence of long-term symptoms varies by using different post-COVID-19 definitions in positively and negatively tested adults: the PRIME post-COVID study

Abstract
Background
Long-term symptoms after a SARS-CoV-2 infection (i.e., post-COVID-19 condition or long COVID), constitute a substantial public health problem. Yet, the prevalence remains currently unclear as different case definitions are used, and negatively tested controls are lacking. We aimed to estimate post-COVID-19 condition prevalence using six definitions.

Methods
The Prevalence, Risk factors, and Impact Evaluation (PRIME) post-COVID-19 condition study is a population-based sample of COVID-19 tested adults. End 2021, 61,655 adults were invited to complete an online questionnaire, including 44 symptoms plus a severity score (0-10) per symptom. The prevalence was calculated in both positively and negatively tested adults, stratified by time since their COVID-19 test (3-5, 6-11 or ≥12 months ago).

Results
In positives (n=7,405; 75.6%), the prevalence of long-term symptoms was between 26.9% and 64.1% using the six definitions, while in negatives (n=2,392; 24.4%) the prevalence varied between 11.4% and 32.5%. The prevalence of long-term symptoms potentially accountable to COVID-19 ranged from 17.9% to 26.3%.

Conclusion
There is a (substantial) variation in prevalence estimates by using different definitions as is current practice, showing limited overlap between definitions, indicating that the essential post-COVID-19 condition criteria are still unclear. Including negatives is important to determine long-term symptoms accountable to COVID-19. Trial registration ClinicalTrials.gov Identifier: NCT05128695.


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I hope the researchers become more aware of the limitations of such studies. People that haven't had Covid hardly exist anymore. Even at the End of 2021 it's not unlikely that less than 50% of controls are actually controls. I don't know how this can be managed. Of course there can be the control group of people which had any of the following types of infection: asymptomatic, not noticed, infected but test was negative or no infection, but is that of as much use?

Controls are urgently needed. But how?

 

I'm not able to comprehend that. We need good controls now, what is the impact of future generations on that?

 

The general population will keep on getting infected, most probably without any effects for the vast majority of them. I think it's unrealistic to hope or demand anything else. I believe things such as ultra-sensitive single molecule array assays, maybe even PET scans, old data and other things I haven't thought of could maybe be beneficial when looking for the type of controls the above study is looking for. Or at least using this data to infere on the construction of controls.

I can't say much about the specifics of Walt's assay and its accuracy and sensitivity, but I do remember him saying that even in the studies with more stringent controls without any supposed infection, where they even run advanced tests and where for example a negative test result when testing for the presence of the N-protein is a pre-requisite, they often find evidence of an infection in 90% of people using their assay.

Having controls in 30 years will have very little impact on anybody on this sub. I certainly hope research will find ways for some form of decent controls now, not when some decades have passed.

 

There is likely a natural variation on top of the poverty of most studies. There is no reason why Long Covid should be evenly distributed across the population. There are many factors having to do with local variants, how much they spread and many other factors that naturally lead to variations.

And no controls anymore, the main epidemiological tool for this. Which is probably the biggest factor given asymptomatic infections.